Test

Introduction

Getting back to remembering leaflet

# library(sp)
library(maps)

## Warning: package 'maps' was built under R version 4.1.3

library(maptools)

## Warning: package 'maptools' was built under R version 4.1.3

## Loading required package: sp

## Warning: package 'sp' was built under R version 4.1.3

## Checking rgeos availability: TRUE
## Please note that 'maptools' will be retired by the end of 2023,
## plan transition at your earliest convenience;
## some functionality will be moved to 'sp'.

library(leaflet)

## Warning: package 'leaflet' was built under R version 4.1.3

library(sf)

## Warning: package 'sf' was built under R version 4.1.3

## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.8
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
## x purrr::map()    masks maps::map()

library(dplyr)

districts <- read_sf('schooldistricts.geojson')
districts$school_dist  <- as.numeric(districts$school_dist)

pal <- colorQuantile("YlGn", NULL, n = 4)
# Randomly generate values between 0 and 1 for 33 rows
t <- runif(33)
# Create a dataframe using the districts and random values
v <- data.frame(districts$school_dist, t)

district_popup <- paste0("<strong>School District: </strong>", v$districts.school_dist, "<br>", "<strong>Proportion: </strong>", v$t)

map <- leaflet() %>%
     addTiles() %>%
     addProviderTiles(providers$CartoDB.Positron) %>%
     setView(lng = -73.935242, lat = 40.730610, zoom = 9) %>%
     addPolygons(data=districts, fillColor=~pal(t), color="darkgrey", weight=1, popup=district_popup)
map

school_info <- read_csv('2018-2021_school_information.csv')

## Rows: 1906 Columns: 17
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (11): schooldbn, name, neighborhood, address, totalstudents, ellprograms...
## dbl  (6): district, coursepassrate, elaprof, mathprof, surveysafety, year
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

# School offers have the offers for middle schools
school_offers <- read_csv('school_offers.csv')

## Rows: 470 Columns: 28
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr  (6): dbn, name, telephone, address, Borough, url
## dbl (22): district, Postcode, Latitude, Longitude, 2016_student_count, 2016_...
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

school_offers$Postcode <- as.character(school_offers$Postcode)
school_offers <- subset(school_offers, select = -c(`name`, `telephone`, `address`, `2016_student_count`, `2016_testers_count`,`2016_offers_count`,`2017_student_count`,`2017_testers_count`,`2017_offers_count`))

# Drop rows that have totalstudents == NA
school_info <- school_info %>% drop_na(totalstudents )

# store the 2018 schools
schools2018 <- school_info[school_info$year == 2018, ]

# Store the 2018 offers
schooloffers2018 <-  subset(school_offers, select = c(`dbn`, `district`, `Postcode`, `Borough`, `url`, `Latitude`, `Longitude`, `2018_student_count`, `2018_testers_count`, `2018_offers_count`))

# Merge the 2018
joindataset <- merge(schools2018, schooloffers2018, by.x = 'schooldbn', by.y='dbn')

# Organizes by districts
by_district <-  schooloffers2018  %>% 
    filter(!is.na(`2018_student_count`)) %>%
    filter(!is.na(`2018_testers_count`)) %>%
    filter(!is.na(`2018_offers_count`)) %>%
    group_by(district) %>%
    summarise(total_students = sum(`2018_student_count`),
              total_testers = sum(`2018_testers_count`),
              total_offers = sum(`2018_offers_count`))

m <- data.frame(by_district)
m$average_acceptance <- m$total_offers / m$total_testers
temp <- select(m, c(district, average_acceptance))

districts_number <- districts$school_dist
districts_number <- data.frame(districts_number)
temp2 <- merge(districts_number, temp, by.x = 'districts_number', by.y='district')

district_popup <- paste0("<strong>School District: </strong>", temp2$districts_number, "<br>", "<strong>Proportion: </strong>", temp2$average_acceptance)

map <- leaflet() %>%
     addTiles() %>%
     addProviderTiles(providers$CartoDB.Positron) %>%
     setView(lng = -73.935242, lat = 40.730610, zoom = 9) %>%
     addPolygons(data=districts, fillColor=~pal(temp2$average_acceptance), color="darkgrey", weight=1, popup=district_popup)
map